Advanced Data Engineering for Life Cycle Applications

A large amount of regularly updated environmental data from governmental sources is publicly available, providing a potentially valuable source of inputs for life cycle applications. For example, these data can be leveraged to generate life cycle inventories for key sectors and commodities and build complex sector attribution models to facilitate scope 3 emissions accounting. However, accessing these data across disparate sources is time-consuming and requires significant expertise. EPA sought ERG’s help in enhancing the availability and usability of this treasure trove of data. 

In collaboration with EPA, ERG has been designing and developing a suite of novel open-source life cycle assessment tools and models, based in Python and R, that mine and harmonize existing large government data sources for use in life cycle applications, such as carbon footprinting and consumption-based GHG emissions inventories. This publicly available ecosystem of LCA tools and models enables users to conduct robust and transparent calculations of supply chain environmental impacts, such as greenhouse gas emissions. To help spread awareness of these tools, ERG staff co-authored four papers with EPA and others to describe these tools in a special “Advanced Data Engineering for Life Cycle Applications” issue of the journal Applied Sciences:

• useeior: An Open-Source R Package for Building and Using U.S. Environmentally-Extended Input–Output Models
• A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data
• FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries
• Life Cycle Data Interoperability Improvements through Implementation of the Federal LCA Commons Elementary Flow List This work is a start. ERG and EPA will continue their collaboration to develop and add to this suite of tools over time.